The Stochastic Dynamic Vehicle Allocation problem involves managing a fleet of vehicles over time in an uncertain demand environment to maximize expected total profits. The problem is formulated as a Stochastic Programming problem. A new heuristic algorithm is developed and is contrasted to various deterministic approximations. The paper presents computational results that were obtained by employing a Rolling Horizon Procedure to simulate the operation of the truckload carrier. Results indicate the superiority of the new algorithm over other approaches tested.

We consider a class of multistage stochastic programming problems that can be formulated as networks with random arc capacities. Large problems have proved intractable using exact methods and hence various approximations have been proposed, ranging from approximating the recourse function to sampling a small number of scenarios to capture future uncertainties. We explore the use of specialized recourse strategies that are not as general as network recourse but nonetheless capture some of the important tradeoffs. These new recourse strategies allow us to develop approximations to the recourse function that can be used to solve problems with thousands of random variables. Given these approximations, classical optimization methods can be used. The concept of hierarchical recourse is introduced and used to synthesize and generalize earlier notions of nodal recourse and cyclic recourse.

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